





'''simple docstring'''

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
   from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import MobileViTImageProcessor
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""





       def snake_case__		(				self  :						Union[str, Any]	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			self.config_class(**self.inputs_dict	)
            self.parent.assertTrue(hasattr(lowerCAmelCase__			,					'''hidden_sizes'''	)	)
            self.parent.assertTrue(hasattr(lowerCAmelCase__			,					'''neck_hidden_sizes'''	)	)
            self.parent.assertTrue(hasattr(lowerCAmelCase__			,					'''num_attention_heads'''	)	)

class 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						str			,					lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						Optional[Any]=13			,					lowerCAmelCase__  :						Any=32			,					lowerCAmelCase__  :						Tuple=2			,					lowerCAmelCase__  :						Tuple=3			,					lowerCAmelCase__  :						List[Any]=640			,					lowerCAmelCase__  :						List[str]=4			,					lowerCAmelCase__  :						Optional[Any]="silu"			,					lowerCAmelCase__  :						Optional[int]=3			,					lowerCAmelCase__  :						Optional[int]=32			,					lowerCAmelCase__  :						List[Any]=0.1			,					lowerCAmelCase__  :						List[Any]=0.1			,					lowerCAmelCase__  :						int=0.1			,					lowerCAmelCase__  :						List[str]=0.02			,					lowerCAmelCase__  :						Any=True			,					lowerCAmelCase__  :						Optional[Any]=True			,					lowerCAmelCase__  :						str=10			,					lowerCAmelCase__  :						List[str]=None			,					)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			parent
            _UpperCamelCase							       =			batch_size
            _UpperCamelCase							       =			image_size
            _UpperCamelCase							       =			patch_size
            _UpperCamelCase							       =			num_channels
            _UpperCamelCase							       =			last_hidden_size
            _UpperCamelCase							       =			num_attention_heads
            _UpperCamelCase							       =			hidden_act
            _UpperCamelCase							       =			conv_kernel_size
            _UpperCamelCase							       =			output_stride
            _UpperCamelCase							       =			hidden_dropout_prob
            _UpperCamelCase							       =			attention_probs_dropout_prob
            _UpperCamelCase							       =			classifier_dropout_prob
            _UpperCamelCase							       =			use_labels
            _UpperCamelCase							       =			is_training
            _UpperCamelCase							       =			num_labels
            _UpperCamelCase							       =			initializer_range
            _UpperCamelCase							       =			scope





       def snake_case__		(				self  :						Tuple	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

            _UpperCamelCase							       =			None
            _UpperCamelCase							       =			None
            if self.use_labels:
                 _UpperCamelCase							       =			ids_tensor([self.batch_size]			,					self.num_labels	)
                 _UpperCamelCase							       =			ids_tensor([self.batch_size, self.image_size, self.image_size]			,					self.num_labels	)

            _UpperCamelCase							       =			self.get_config()

            return config, pixel_values, labels, pixel_labels





       def snake_case__		(				self  :						Union[str, Any]	)       ->	List[str]:

            '''simple docstring'''




            return MobileViTConfig(
                image_size=self.image_size			,					patch_size=self.patch_size			,					num_channels=self.num_channels			,					num_attention_heads=self.num_attention_heads			,					hidden_act=self.hidden_act			,					conv_kernel_size=self.conv_kernel_size			,					output_stride=self.output_stride			,					hidden_dropout_prob=self.hidden_dropout_prob			,					attention_probs_dropout_prob=self.attention_probs_dropout_prob			,					classifier_dropout_prob=self.classifier_dropout_prob			,					initializer_range=self.initializer_range			,					)





       def snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						List[Any]	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			MobileViTModel(config=lowerCAmelCase__	)
            model.to(lowerCAmelCase__	)
            model.eval()
            _UpperCamelCase							       =			model(lowerCAmelCase__	)
            self.parent.assertEqual(
                result.last_hidden_state.shape			,					(
                    self.batch_size,
                    self.last_hidden_size,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )			,					)





       def snake_case__		(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						Optional[int]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			self.num_labels
            _UpperCamelCase							       =			MobileViTForImageClassification(lowerCAmelCase__	)
            model.to(lowerCAmelCase__	)
            model.eval()
            _UpperCamelCase							       =			model(lowerCAmelCase__			,					labels=lowerCAmelCase__	)
            self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_labels)	)





       def snake_case__		(				self  :						List[str]			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						int	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			self.num_labels
            _UpperCamelCase							       =			MobileViTForSemanticSegmentation(lowerCAmelCase__	)
            model.to(lowerCAmelCase__	)
            model.eval()
            _UpperCamelCase							       =			model(lowerCAmelCase__	)
            self.parent.assertEqual(
                result.logits.shape			,					(
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )			,					)
            _UpperCamelCase							       =			model(lowerCAmelCase__			,					labels=lowerCAmelCase__	)
            self.parent.assertEqual(
                result.logits.shape			,					(
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )			,					)







       def snake_case__		(				self  :						Optional[int]	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.prepare_config_and_inputs()
            _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase							       =			config_and_inputs
            _UpperCamelCase							       =			{'''pixel_values''': pixel_values}
            return config, inputs_dict

@require_torch
class 							__lowerCAmelCase	(       __magic_name__		,		__magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	List[str]									=       (
           (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
           if is_torch_available()
           else ()
       )
       _snake_case      :	Any									=       (
           {
               'feature-extraction': MobileViTModel,
               'image-classification': MobileViTForImageClassification,
               'image-segmentation': MobileViTForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       _snake_case      :	List[Any]									=       False
       _snake_case      :	Tuple									=       False
       _snake_case      :	Dict									=       False
       _snake_case      :	List[str]									=       False





       def snake_case__		(				self  :						Union[str, Any]	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			MobileViTModelTester(self	)
            _UpperCamelCase							       =			MobileViTConfigTester(self			,					config_class=lowerCAmelCase__			,					has_text_modality=lowerCAmelCase__	)





       def snake_case__		(				self  :						Any	)       ->	Optional[Any]:

            '''simple docstring'''




            self.config_tester.run_common_tests()





       @unittest.skip(reason='''MobileViT does not use inputs_embeds'''	)
       def snake_case__		(				self  :						Optional[Any]	)       ->	Union[str, Any]:

            '''simple docstring'''




            pass





       @unittest.skip(reason='''MobileViT does not support input and output embeddings'''	)
       def snake_case__		(				self  :						Tuple	)       ->	List[Any]:

            '''simple docstring'''




            pass





       @unittest.skip(reason='''MobileViT does not output attentions'''	)
       def snake_case__		(				self  :						Dict	)       ->	List[str]:

            '''simple docstring'''




            pass





       def snake_case__		(				self  :						List[Any]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _UpperCamelCase							       =			model_class(lowerCAmelCase__	)
                 _UpperCamelCase							       =			inspect.signature(model.forward	)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 _UpperCamelCase							       =			[*signature.parameters.keys()]

                 _UpperCamelCase							       =			['''pixel_values''']
                 self.assertListEqual(arg_names[:1]			,					lowerCAmelCase__	)





       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
       def snake_case__		(				self  :						str	)       ->	Any:

            '''simple docstring'''




            pass





       def snake_case__		(				self  :						Tuple	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__	)





       def snake_case__		(				self  :						Optional[int]	)       ->	List[str]:

            '''simple docstring'''




            def check_hidden_states_output(lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						Optional[int]	):
                 _UpperCamelCase							       =			model_class(lowerCAmelCase__	)
                 model.to(lowerCAmelCase__	)
                 model.eval()

                 with torch.no_grad():
                      _UpperCamelCase							       =			model(**self._prepare_for_class(lowerCAmelCase__			,					lowerCAmelCase__	)	)

                 _UpperCamelCase							       =			outputs.hidden_states

                 _UpperCamelCase							       =			5
                 self.assertEqual(len(lowerCAmelCase__	)			,					lowerCAmelCase__	)

                 # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
                 # with the width and height being successively divided by 2.
                 _UpperCamelCase							       =			2
                 for i in range(len(lowerCAmelCase__	)	):
                      self.assertListEqual(
                          list(hidden_states[i].shape[-2:]	)			,					[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]			,					)
                      divisor *= 2

                 self.assertEqual(self.model_tester.output_stride			,					divisor // 2	)

            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _UpperCamelCase							       =			True
                 check_hidden_states_output(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 _UpperCamelCase							       =			True

                 check_hidden_states_output(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Tuple	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__	)





       def snake_case__		(				self  :						Optional[Any]	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase__	)







       @slow
       def snake_case__		(				self  :						str	)       ->	str:

            '''simple docstring'''




            for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _UpperCamelCase							       =			MobileViTModel.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)







def    a__							(							)						->						List[str]:





     """simple docstring"""

     _UpperCamelCase							       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
     return image




@require_torch
@require_vision
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       @cached_property
       def snake_case__		(				self  :						Any	)       ->	List[Any]:

            '''simple docstring'''




            return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small'''	) if is_vision_available() else None





       @slow
       def snake_case__		(				self  :						List[str]	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''	).to(lowerCAmelCase__	)

            _UpperCamelCase							       =			self.default_image_processor
            _UpperCamelCase							       =			prepare_img()
            _UpperCamelCase							       =			image_processor(images=lowerCAmelCase__			,					return_tensors='''pt'''	).to(lowerCAmelCase__	)

            # forward pass
            with torch.no_grad():
                 _UpperCamelCase							       =			model(**lowerCAmelCase__	)

            # verify the logits
            _UpperCamelCase							       =			torch.Size((1, 1000)	)
            self.assertEqual(outputs.logits.shape			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			torch.tensor([-1.9364, -1.2327, -0.4653]	).to(lowerCAmelCase__	)

            self.assertTrue(torch.allclose(outputs.logits[0, :3]			,					lowerCAmelCase__			,					atol=1e-4	)	)





       @slow
       def snake_case__		(				self  :						Optional[int]	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''	)
            _UpperCamelCase							       =			model.to(lowerCAmelCase__	)

            _UpperCamelCase							       =			MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''	)

            _UpperCamelCase							       =			prepare_img()
            _UpperCamelCase							       =			image_processor(images=lowerCAmelCase__			,					return_tensors='''pt'''	).to(lowerCAmelCase__	)

            # forward pass
            with torch.no_grad():
                 _UpperCamelCase							       =			model(**lowerCAmelCase__	)
            _UpperCamelCase							       =			outputs.logits

            # verify the logits
            _UpperCamelCase							       =			torch.Size((1, 21, 32, 32)	)
            self.assertEqual(logits.shape			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			torch.tensor(
                [
                    [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
                    [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
                    [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
                ]			,					device=lowerCAmelCase__			,					)

            self.assertTrue(torch.allclose(logits[0, :3, :3, :3]			,					lowerCAmelCase__			,					atol=1e-4	)	)







       @slow
       def snake_case__		(				self  :						Optional[Any]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''	)
            _UpperCamelCase							       =			model.to(lowerCAmelCase__	)

            _UpperCamelCase							       =			MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''	)

            _UpperCamelCase							       =			prepare_img()
            _UpperCamelCase							       =			image_processor(images=lowerCAmelCase__			,					return_tensors='''pt'''	).to(lowerCAmelCase__	)

            # forward pass
            with torch.no_grad():
                 _UpperCamelCase							       =			model(**lowerCAmelCase__	)

            _UpperCamelCase							       =			outputs.logits.detach().cpu()

            _UpperCamelCase							       =			image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__			,					target_sizes=[(50, 60)]	)
            _UpperCamelCase							       =			torch.Size((50, 60)	)
            self.assertEqual(segmentation[0].shape			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__	)
            _UpperCamelCase							       =			torch.Size((32, 32)	)
            self.assertEqual(segmentation[0].shape			,					lowerCAmelCase__	)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase    :   int						=			{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			['ConditionalDetrFeatureExtractor']
		lowercase    :   int						=			['ConditionalDetrImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   Optional[int]						=			[
		    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ConditionalDetrForObjectDetection',
		    'ConditionalDetrForSegmentation',
		    'ConditionalDetrModel',
		    'ConditionalDetrPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

		lowercase    :   Optional[int]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)